Fifty years of structural equation modeling: A history of generalization, unification, and diffusion

Bollen, Kenneth A.; Fisher, Zachary; Lilly, Adam G.; Brehm, Christopher; Luo, Lan; Martinez, Alejandro; Ai, Ye

doi:10.1016/j.ssresearch.2022.102769

Cited by 15 publications

(4 citation statements)

References 135 publications

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“…LCS modeling was used to estimate true changes in 4 bodily pain measures and WOMAC pain scores from preoperatively to 12 months postoperatively [26][27][28] . LCS estimates are equivalent to observed difference scores between preoperative and postoperative measurements when outcomes are measured without random error.…”

Section: Discussionmentioning

confidence: 99%

A Latent Change Score Approach to Understanding Chronic Bodily Pain Outcomes Following Knee Arthroplasty

Riddle,

Dumenci

2023

Journal of Bone and Joint Surgery

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Background: The extent to which chronic bodily pain changes following total knee arthroplasty (TKA) is unknown. We determined the extent of chronic bodily pain changes at 1 year following TKA. Methods: Data from our randomized trial of pain coping skills, which revealed no effect of the studied interventions, were used. The presence and severity of chronic pain in 16 body regions, excluding the surgically treated knee, were determined prior to and 1 year following surgery. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain scale was used to quantify the extent of surgical knee pain. Latent change score (LCS) models were used to determine the extent to which true chronic bodily pain scores change after TKA. Results: The mean age of the sample of 367 participants was 63.4 ± 8.0 years, and 247 (67%) were female. LCS analyses showed significant 20% to 54% reductions in pain in the surgically treated lower limb (not including the surgically treated knee), pain in the non-surgically treated lower limb, and whole body pain. In bivariate LCS analyses, greater improvement in the WOMAC pain score, indicating surgical benefit of TKA, led to greater improvement in all 4 bodily pain areas beyond the surgically treated knee, even after controlling for the latent change in pain catastrophizing. Conclusions: Clinically important chronic bodily pain reductions occurred following TKA and may be causally linked to the surgical procedure. Reduction in chronic bodily pain in sites other than the surgically treated knee is an additional benefit of TKA. Level of Evidence: Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.

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Section: Discussionmentioning

confidence: 99%

A Latent Change Score Approach to Understanding Chronic Bodily Pain Outcomes Following Knee Arthroplasty

Riddle,

Dumenci

2023

Journal of Bone and Joint Surgery

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“…SEM can include latent variables for which no observation is available, and these might include composite variables representing general theoretical concepts for which no direct measurement is possible (Grace & Bollen, 2008). SEM can also clarify the circumstances when a given causal effect is estimable, and if not, what additional ‘instrumental variable’ might render it estimable (Bollen et al., 2022). If the specified structural equation does not include loops (i.e.…”

Section: Introductionmentioning

confidence: 99%

Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms

Thorson,

Andrews,

Essington

et al. 2024

Methods Ecol Evol

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Ecological analyses typically involve many interacting variables. Ecologists often specify lagged interactions in community dynamics (i.e. vector‐autoregressive models) or simultaneous interactions (e.g. structural equation models), but there is less familiarity with dynamic structural equation models (DSEM) that can include any simultaneous or lagged effect in multivariate time‐series analysis. We propose a novel approach to parameter estimation for DSEM, which involves constructing a Gaussian Markov random field (GMRF) representing simultaneous and lagged path coefficients, and then fitting this as a generalized linear mixed model to missing and/or non‐normal data. We provide a new R‐package dsem, which extends the ‘arrow interface’ from path analysis to represent user‐specified lags when constructing the GMRF. We also outline how the resulting nonseparable precision matrix can generalize existing separable models, for example, for time‐series and species interactions in a vector‐autoregressive model. We first demonstrate dsem by simulating a two‐species vector‐autoregressive model based on wolf–moose interactions on Isle Royale. We show that DSEM has improved precision when data are missing relative to a conventional dynamic linear model. We then demonstrate DSEM via two contrasting case studies. The first identifies a trophic cascade where decreased sunflower starfish has increased urchin and decreased kelp densities, while sea otters have a simultaneous positive effect on kelp in the California Current from 1999 to 2018. The second estimates how declining sea ice has decreased cold‐water habitats, driving a decreased density for fall copepod predation and inhibiting early‐life survival for Alaska pollock from 1963 to 2023. We conclude that DSEM can be fitted efficiently as a GLMM involving missing data, while allowing users to specify both simultaneous and lagged effects in a time‐series structural model. DSEM then allows conceptual models (developed with stakeholder input or from ecological expertise) to be fitted to incomplete time series and provides a simple interface for granular control over the number of estimated time‐series parameters. Finally, computational methods are sufficiently simple that DSEM can be embedded as component within larger (e.g. integrated population) models. We therefore recommend greater exploration and performance testing for DSEM relative to familiar time‐series forecasting methods.

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“…Structural equation modeling has evolved significantly since the advent of linear path analysis, offering greater flexibility to accommodate non-linearities and interactions (Bollen, 1989;Bollen et al, 2022;Kline, 2023;Winship and Mare, 1983), but these advances come with a caveat. Most applications still compel researchers to specify the exact functional form of the causal relationships under study.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning with DAGs

Balgi,

Daoud,

Pena

et al. 2024

Preprint

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Social science theories often postulate causal relationships among a set of variables or events. Although directed acyclic graphs (DAGs) are increasingly used to represent these theories, their full potential has not yet been realized in practice. As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships. Nevertheless, to simplify the task of empirical evaluation, researchers tend to invoke such assumptions anyway, even though they are typically arbitrary and do not reflect any theoretical content or prior knowledge. Moreover, functional form assumptions can engender bias, whenever they fail to accurately capture the complexity of the causal system under investigation. In this article, we introduce causal-graphical normalizing flows (cGNFs), a novel approach to causal inference that leverages deep neural networks to empirically evaluate theories represented as DAGs. Unlike conventional approaches, cGNFs model the full joint distribution of the data according to a DAG supplied by the analyst, without relying on stringent assumptions about functional form. In this way, the method allows for flexible, semi-parametric estimation of any causal estimand that can be identified from the DAG, including total effects, conditional effects, direct and indirect effects, and path-specific effects. We illustrate the method with a reanalysis of Blau and Duncan’s (1967) model of status attainment and Zhou’s (2019) model of conditional versus controlled mobility. To facilitate adoption, we provide open-source software together with a series of online tutorials for implementing cGNFs. The article concludes with a discussion of current limitations and directions for future development.

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Fifty years of structural equation modeling: A history of generalization, unification, and diffusion

Cited by 15 publications

References 135 publications

A Latent Change Score Approach to Understanding Chronic Bodily Pain Outcomes Following Knee Arthroplasty

A Latent Change Score Approach to Understanding Chronic Bodily Pain Outcomes Following Knee Arthroplasty

Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms

Deep Learning with DAGs

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